# Copyright (c) 2019 NVIDIA Corporation
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from six.moves import range

from .text2speech import Text2Speech


class Text2SpeechCentaur(Text2Speech):
  """
  Text-to-speech data layer for Centaur.
  """

  def get_alignments(self, attention_mask):
    alignments_name = ["dec_enc_alignment"]

    specs = []
    titles = []

    for name, alignment in zip(alignments_name, attention_mask):
      for layer in range(len(alignment)):
        for head in range(alignment.shape[1]):
          specs.append(alignment[layer][head])
          titles.append("{}_layer_{}_head_{}".format(name, layer, head))

    return specs, titles
